Define an Estimator Specification

Define the estimator specification, used as part of the model_fn defined with custom estimators created by estimator(). See estimator() for more details.

estimator_spec(mode, predictions = NULL, loss = NULL, train_op = NULL,
  eval_metric_ops = NULL, training_hooks = NULL, evaluation_hooks = NULL,
  training_chief_hooks = NULL, ...)

Arguments

mode

A key that specifies whether we are performing training ("train"), evaluation ("eval"), or prediction ("infer"). These values can also be accessed through the mode_keys() object.
predictions

The prediction tensor(s).
loss

The training loss tensor. Must be either scalar, or with shape c(1).
train_op

The training operation -- typically, a call to optimizer$minimize(...), depending on the type of optimizer used during training.
eval_metric_ops

A list of metrics to be computed as part of evaluation. This should be a named list, mapping metric names (e.g. "rmse") to the operation that computes the associated metric (e.g. tf$metrics$root_mean_squared_error(...)). These metric operations should be evaluated without any impact on state (typically is a pure computation results based on variables). For example, it should not trigger the update ops or requires any input fetching.
training_hooks

(Available since TensorFlow v1.4) A list of session run hooks to run on all workers during training.
evaluation_hooks

(Available since TensorFlow v1.4) A list of session run hooks to run during evaluation.
training_chief_hooks

(Available since TensorFlow v1.4) A list of session run hooks to run on chief worker during training.
...

Other optional (named) arguments, to be passed to the EstimatorSpec constructor.

See also

Other custom estimator methods: estimator, evaluate.tf_estimator, export_savedmodel.tf_estimator, predict.tf_estimator, train.tf_estimator